{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single-phase flow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we investigate how to solve single-phase flow in PorePy. <br>\n",
    "We consider\n",
    "* two-point flux approximation\n",
    "* multi-point flux approximation\n",
    "* dual virtual element method\n",
    "\n",
    "**Note**: This tutorial will focus on a 2d domain, however most of the code works for 1d, 2d, and 3d domains. The case of a 2d domain with an immersed 1d fracture is treated below. <br>\n",
    "\n",
    "## Problem statement\n",
    "\n",
    "Let $\\Omega$ be a regular domain with boundary $\\partial \\Omega$. The boundary can be divided in two non-overlapping parts useful to impose Dirichlet ($\\partial \\Omega_d$) and Neumann ($\\partial \\Omega_n$) boundary conditions. We indicate with $\\mathbf{n}$ the outward unit normal vector of $\\partial \\Omega$.<br>\n",
    "The single-phase flow can be written in\n",
    "* dual formulation as:\n",
    "$$\\nabla \\cdot \\mathbf{u} = f \\qquad \\mathbf{u} = - K \\nabla p$$\n",
    "with boundary conditions on $\\partial \\Omega_d$ and $\\partial \\Omega_n$:\n",
    "$$ p = p_b \\qquad \\mathbf{u} \\cdot \\mathbf{n} = u_b$$\n",
    "* primal formulation as:\n",
    "$$ - \\nabla \\cdot K \\nabla p = f $$\n",
    "with boundary conditions on $\\partial \\Omega_d$ and $\\partial \\Omega_n$:\n",
    "$$ p = p_b \\qquad - K \\nabla p \\cdot \\mathbf{n} = u_b$$\n",
    "\n",
    "Where $f$ is a scalar source/sink term, $K$ is the permeability matrix, $p_b$ is the pressure at the boundary (Dirichlet condition), and $u_b$ is the flux at the boundary (Neumann condition).<br>\n",
    "\n",
    "We present *step-by-step* how to create the grid, declare the problem data, and finally solve the problem. <br><br>\n",
    "\n",
    "For the example we assume: $\\Omega$ as presented below, $\\partial \\Omega_d = \\partial \\Omega$, $\\partial \\Omega_n = \\emptyset$, with data: $f = 1$, $K = I$ and $p_b = 0$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import basic modules"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before creating the grid we import NumPy, the SciPy sparse library and PorePy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivar/Dropbox/porepy/src/porepy/grids/partition.py:19: UserWarning: Could not import pymetis. Some functions will not work as    intended\n",
      "  intended\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.sparse as sps\n",
    "import porepy as pp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creation of the grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We specify number of cells in each dimension and the physical size of the domain. Then we create a Cartesian grid and compute geometric properties such as face centers, cell volumes etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Nx = Ny = 20\n",
    "phys_dims = [1,1]\n",
    "g = pp.CartGrid([Nx, Ny], phys_dims)\n",
    "g.compute_geometry()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the grid using the interface with matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivar/.local/lib/python3.7/site-packages/mpl_toolkits/mplot3d/axes3d.py:753: UserWarning: Attempting to set identical bottom==top results\n",
      "in singular transformations; automatically expanding.\n",
      "bottom=0.0, top=0.0\n",
      "  'bottom=%s, top=%s') % (bottom, top))\n"
     ]
    },
    {
     "data": {
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LjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgB42saNG1VUVKRAIKCmpiYVFhZq+/bt8a4FAOgFHGPM+a4/75UAAHQHTzzxhAKBgFpaWpSTk6PHHnss3pUAAD2H0+4VjCkAgNcFg0EVFxcrJSVF69atU2JiYrwrAQB6jnbHFA/zQ68SjUZ1gR8gAPCgw4cPq7GxUSdPnlQgEIh3HQBAL8GZKfQaxhgFAgElJibK7/fLcdr9IQMAjykpKdGsWbNUXV2t/fv3a9GiRfGuBADoOdr9ptHXlS2A7mDPnj0aNmyY/H6/EhI4OQt43SuvvCK/36+vfOUrikQimjx5st5++23ddNNN8a4GAOjhODOFXuP0mamysjJNnDhRkuT3++Xz8TMFAAAAtIvnTAFnSkhIkOM4qq6uVigU4nlUAAAAuGiMKfRajuOorq5O4XBYwWCQQQUAAICLwphCr+c4jqLRqHbt2qVoNBrvOgAAAPAIxhR6PcdxlJCQoLq6OrW2tiocDnOWCgAAABfEmEKvcvz48fMOJcdxFAqFeB4VAAAALogxhV6lsbFRzc3NamxsPOf1juPIcRzt2bNHwWCQh/0BAACgXYwp9CrZ2dlKSUnRtm3bVF9ff85jTr8wRTQajT3sDwAAAPg0xhR6ncTERI0fP14NDQ1qaWlRJBI553Gd8fLppaWlGj16tPLz87Vw4cKzrm9tbdXMmTOVn5+vCRMmqKam5qI/R290ofv1ueeeU0FBgYqKinTzzTdrz549cWjpPRe6X0/7/e9/L8dxVFFR0YXtvKsj9+trr72mgoICFRYW6itf+UoXN/SeC92ne/fu1ZQpU3TttdeqqKhIK1eujENL75kzZ44uvfRSjRkz5pzXG2P00EMPKT8/X0VFRdq8eXMXNwS6IWPM+f4APUY0GjXNzc3m7bffNoFAwLS0tJjS0lLzzjvvmIaGhtj7A4HAWW83NzebQCBgotFohz9fOBw2eXl55uOPPzatra2mqKjIfPjhh22O+cUvfmEefPBBY4wxS5cuNTNmzOjU29wTdeR+ffvtt01TU5Mxxpjnn3+e+7UDOnK/GmPMiRMnzA033GAmTJhgNm7cGIem3tKR+3Xnzp3mmmuuMUeOHDHGGHPw4MF4VPWMjtynDzzwgHn++eeNMcZ8+OGHZvjw4XFo6j3vvvuu2bRpkyksLDzn9W+++aa59dZbTTQaNevXrzfjx4/v4oZA3LS7lzgzhV7LcRwlJSVpzJgx2rZtm0Kh0HmPvdiXTy8vL1d+fr7y8vKUlJSkWbNmafny5W2OWb58ue69915J0vTp07VmzRpe+OICOnK/TpkyRampqZKkiRMnqq6uLh5VPaUj96skPfnkk3r00UeVkpISh5be05H79YUXXtDcuXOVmZkpSbr00kvjUdUzOnKfOo6jEydOSDr1wkNDhw6NR1XP+dznPqf+/fu3e/3y5ct1zz33yHEcTZw4UceOHdP+/fu7sCHQ/TCm0Oulp6dr/PjxCofD+uCDD875HCmbl0+vr69Xbm5u7HJOTs5Zz9M68xifz6eMjAwdPny4E25Vz9WR+/VMixcv1he+8IWuqOZpHblfN2/erNraWn3xi1/s6nqe1ZH7defOndq5c6euv/56TZw4UaWlpV1d01M6cp8+9dRTWrJkiXJycjRt2jT9/Oc/7+qaPdLFfv0FegPGFKBTQ6ZPnz7KzMxUeXn5ec8+dcbLp9966622VXEen75flyxZooqKCj3yyCNxatQz3HrrrYpGo3r44Yf17LPPxrtOj3H672s4HFZVVZXWrl2rpUuX6oEHHtCxY8fi3M67br31Vi1dulSzZ89WXV2dVq5cqbvvvptXZ/2M+HcLODfGFHCGnJwcXXXVVWpubm73oWEdffn07Oxs1dbWxi7X1dUpOztbktTQ0HDWMeFwWMePH9eAAQM68yb1OB25XyVp9erVeuaZZ7RixQolJyd3eU+vudD9evLkSW3fvl033nijRowYobKyMpWUlPAiFBfQkb+vOTk5Kikpkd/v18iRIzVq1ChVVVXFpa8XnO8+lU7dr4sXL9aMGTMkSZMmTVIgEGjz9QEXr6Gh4YL3PdAbMaaAT0lPT1daWpqOHDmibdu2nfPsU0dePr24uFhVVVWqrq5WMBjUsmXLVFJS0uaYkpIS/eY3v5Ekvf7667rpppvkOI47N6yH6Mj9umXLFj344INasWIFzz/poAvdrxkZGWpoaFBNTY1qamo0ceJErVixQuPGjYtj6+6vI39fb7vtNq1du1bSqW9Yd+7cqby8vDi09YaO3KfDhg3TmjVrJEk7duxQIBDQwIED41G3RykpKdErr7wiY4zKysqUkZGhIUOGxLsWEFe+eBcAuiPHcVRUVKS6ujodOHBAJ0+eVHp6+lnHJSQkyBij6upqjRgxQj6fLzaGfD6fFi1apKlTpyoSiWjOnDkqLCzUvHnzYg/hue+++3T33XcrPz9f/fv317Jly7r0dnpRR+7XRx55RI2NjbrjjjsknfrGasWKFfGs3e115H7FxevI/Tp16lT9+c9/VkFBgRITE/XjH/+YM9Tncb779PS4f/bZZ/XAAw/oJz/5iRzH0csvv8wPqjrgzjvv1Nq1a9XQ0KCcnBw9/fTTbV6cadq0aVq5cqXy8/OVmpqql156KY5tge7BucBzPnhZMfQYxhgFAgGVlZVp8uTJkqR169Zd8O2//OUvSkxM1LBhw7R37952j///Skp0tLGx03v7JbX/OoO9L9uLnd3MprP3s+ns/WwvdnYzOzM9XUf+79UUgR6i3Z/GcGYKuIDTv+R3x44damlpUTgcls939v86RxsbXfnpgyP3fqrhxWwvdnYzm87ez6az97O92NnNbOfkSRdSge6J50wBHeDz+XTVVVfJ5/OpvLxcJ/mHAgAAoNdjTAEXwe/3q6ioSNu3b1cwGOQX7AIAAPRijCngIvXt21fjx49XJBLRtm3bzvlKfgAAAOj5GFOAhcTERPXp00cDBw5UeXm5IpFIvCsBAACgizGmgM9g6NChKioqUiAQiHcVAAAAdDHGFPAZ9e3bV6mpqfGuAQAAgC7GS6MDnYBfBgkAwP9z+hcon5aVlaXS0tI4tQHcw5kpAAAAdKqKiorYn6KiIm3evFljxow557HGGD300EPKz8+PHQt4BWMKAAAArpk9e/Z5z0qtWrVKVVVVqqqq0q9+9St9/etf78J2wGfDmAIAAIBrPve5z6l///7tXr98+XLdc889chxHEydO1LFjx7R///4ubAjYY0wBAAAgburr65Wbmxu7nJOTo/r6+jg2AjqOMQUAAAAAFhhTAAAAiJvs7GzV1tbGLtfV1Sk7OzuOjYCOY0wBAAAgbkpKSvTKK6/IGKOysjJlZGRoyJAh8a4FdAi/ZwoAAACuufPOO7V27Vo1NDQoJydHTz/9tEKhkCTpa1/7mqZNm6aVK1cqPz9fqampeumll+LcGOg4xhTQSfyS3PjVvT6Xcr2a7cXObmbT2fvZdPZ+thc7u5nt/9TlpUuXnvd4x3H0i1/8woUmgPsYU0AnCUkyLuQ6LuV6NduLnd3MprP3s+ns/WwvdnYz263xB3RHPGcKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAgi/eBYCewi/JcSHX51KuV7O92NnNbDp7P5vO3s/2Ymc3s/0uZALdFWMK6CQhScaFXMelXK9me7Gzm9l09n42nb2f7cXObma7Nf6A7oiH+QEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABV+8CwA9hV+S40Kuz6Vcr2Z7sbOb2XT2fjadvZ/txc5uZvtdyAS6K8YU0ElCkowLuY5LuV7N9mJnN7Pp7P1sOns/24ud3cx2a/wB3REP8wMAAAAAC4wpAAAAALDAmAIAAAAAC4wpAAAAALDAmAIAAAAAC4wpAAAAALDAmAIAAAAAC4wpAAAAALDAmAIAAAAAC4wp9CrRaDTeFQAAANBDMKbQq9TU1KipqUmHDh2SMSbedQAAAOBhvngXALpSXl6eDhw4oIMHD6qqqkrBYFDRaFQJCfxcAQAAABeH7yDR6yQkJGjMmDG67rrrZIzRunXr9PHHH3OmCgAAABeFMYVeKzk5WcnJyZo0aZKSkpLU1NSkyspKNTU1xbsaAAAAPIAxhV4vMTFRubm56tu3r7KysvThhx+qublZR44c4WwVAAAA2sWYAs5w6aWXavz48UpOTlZtba02bNigUCjEqwACAADgLLwABXAOiYmJuvrqq9XS0qL169dr/fr1ys7OPu+ZKr8kx4UuPpdyvZrtxc5uZtPZ+9l09n62Fzu7me13IRPorhhTwHn06dNHKSkpGj9+vOrq6tTU1KS//e1vGj58+FnHhiS58aBAx6Vcr2Z7sbOb2XT2fjadvZ/txc5uZrs1/oDuiIf5AR3g8/k0YsQI9e3bVxkZGdq6dataWlp0/PjxeFcDAABAnHBmCrhIQ4YM0eDBg/Xee+/p448/VjgcVjgcjnctAAAAdDHOTAEWHMdRYmKixo4dq8LCQsYUAABAL8SZKeAzSktLU0pKSrxrAADQbYwbN67N5aysLJWWlsapDeAezkwBAACgU1VUVMT+LFiwQNXV1crPz9fChQvPOnbv3r2aMmWKrr32WhUVFWnlypVxaAzYYUwBAADAFZFIRHPnztWqVatUWVmppUuXqrKyss0xCxYs0IwZM7RlyxYtW7ZM3/jGN+LUFrh4jCkAAAC4ory8XPn5+crLy1NSUpJmzZql5cuXtznGcRydOHFCknT8+HENHTo0HlUBKzxnCgAAAK6or69Xbm5u7HJOTo42bNjQ5pinnnpKn//85/Xzn/9cTU1NWr16dVfXBKxxZgoAAABxs3TpUs2ePVt1dXVauXKl7r77bkWj0XjXAjqEMQUAAABXZGdnq7a2Nna5rq5O2dnZbY5ZvHixZsyYIUmaNGmSAoGAGhoaurQnYIsxBQAAAFcUFxerqqpK1dXVCgaDWrZsmUpKStocM2zYMK1Zs0aStGPHDgUCAQ0cODAedYGLxpgCAACAK3w+nxYtWqSpU6fqyiuv1IwZM1RYWKh58+ZpxYoVkqRnn31WL7zwgq6++mrdeeedevnll+U4TpybAx3DC1AAAADANdOmTdO0adPavG/+/PmxtwsKCvTXv/61q2sBnYIzUwAAAABggTEFAAAAABYYUwAAAABggedMAZ3EL8mNp8v6XMr1arYXO7uZTWfvZ9PZ+9le7Oxmtt+FTKC7YkwBnSQkybiQ67iU69VsL3Z2M5vO3s+ms/ezvdjZzWxehw+9CQ/zAwAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAACCpImPAAAgAElEQVQLjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALvngXAHoKvyTHhVyfS7lezfZiZzez6ez9bDp7P9uLnd3M9ruQCXRXjCmgk4QkGRdyHZdyvZrtxc5uZtPZ+9l09n62Fzu7me3W+AO6Ix7mBwAAAAAWGFMAAAAAYIExBQAAAAAWGFMAAAAAYIExBQAAAAAWGFMAAAAAYIExBQAAAAAWGFMAAAAAYIExBQAAAAAWGFMAAAAAYIExBQAAAAAWGFMAAAAAYIExBQAAAAAWGFMAAAAAYIExBQAAAAAWGFMAAAAAYMEX7wJAT+GX5LiQ63Mp16vZXuzsZjadvZ9NZ+9ne7Gzm9l+FzKB7ooxBXSSkCTjQq7jUq5Xs73Y2c1sOns/m87ez/ZiZzez3Rp/QHfEw/wAAAAAwAJjCgAAAAAsMKYAAAAAwAJjCgAAAAAsMKYAAAAAwAJjCgAAAAAsMKYAAAAAwAJjCgAAAAAsMKYAAAAAwAJjCgAAAAAsMKYAAAAAwAJjCgAAAAAsMKYAAAAAwIIv3gWArrR37161tLRo9+7dSk1NVSQSUSQSUWJiYryrAQAAwGMYU+hVhg4dqtraWvXp00eNjY0KBoMqLy9XNBpVc3OzKisrlZaWpnA4rJaWFqWkpMS7MgAAALopxhR6FZ/Pp8TERA0ZMkSS9Mknn2jSpEkyxmjdunUaPHiwmpqaFA6HtWPHDgUCATU2NmrLli1KS0tTMBjU0aNHlZaWFudbAgAAgHhjTAGSHMeR4zjq37+/+vfvr9raWo0dO1aStG7dOo0ePVpNTU3at2+f9u3bp6amJjU2NqqsrEypqalqbW2VX5LjQjefS7lezfZiZzez6ez9bDp7P9uLnd3M9ruQCXRXjCmgA1JTU5WamqqkpCQVFhZKOjWyxo0bp+bmZh09elQhScaFz+24lOvVbC92djObzt7PprP3s73Y2c1sR9K4cePavC8rK0ulpaUufDYgvng1P+Az8Pl8uuSSS+T383M4AABOq6ioiP1ZsGCBqqurlZ+fr4ULF57z+Ndee00FBQUqLCzUV77ylS5uC9jjzBQAAABcEYlENHfuXL311lvKyclRcXGxSkpKVFBQEDumqqpKP/zhD/XXv/5VmZmZ+uSTT+LYGLg4nJkCAACAK8rLy5Wfn6+8vDwlJSVp1qxZWr58eZtjXnjhBc2dO1eZmZmSpEsvvTQeVQErjCkAAAC4or6+Xrm5ubHLOTk5qq+vb3PMzp07tXPnTl1//fWaOHEiz62Cp/AwPwAAAMRNOBxWVVWV1q5dq7q6On3uc5/TBx98oH79+sW7GnBBnJkCAACAK7Kzs1VbWxu7XFdXp+zs7DbH5OTkqKSkRH6/XyNHjtSoUaNUVVXV1VUBK4wpAAAAuKK4uFhVVVWqrq5WMBjUsmXLVFJS0uaY2267TWvXrpUkNTQ0aOfOncrLy4tDW+DiMaYAAADgCp/Pp0WLFmnq1Km68sorNWPGDBUWFmrevHlasWKFJGnq1KkaMGCACgoKNGXKFP34xz/WgAED4twc6BieMwUAAADXTJs2TdOmTWvzvvnz58fedhxHzz33nJ577rmurgZ8ZpyZAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMCYAgAAAAALjCkAAAAAsMBLowOdxC/JcSHX51KuV7O92NnNbDp7P5vO3s/2Ymc3s/0uZALdFWMK6CQhScaFXMelXK9me7Gzm9l09n42nb2f7cXObma7Nf6A7oiH+QEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAMAT5s2bp5/+9Kexy48//rh+9rOfxbERejvGFAAAADxhzpw5euWVVyRJ0WhUy5Yt01133RXnVujNfPEuAPQUfkmOC7k+l3K9mu3Fzm5m09n72XT2frYXO7uZ7Xch87QRI0ZowIAB2rJliw4ePKhrr71WAwYMcPEzAufHmAI6SUiScSHXcSnXq9le7OxmNp29n01n72d7sbOb2W6Nv9Puv/9+vfzyyzpw4IDmzJnj8mcDzo+H+QEAAMAzbr/9dpWWlmrjxo2aOnVqvOugl+PMFAAAADwjKSlJU6ZMUb9+/ZSYmBjvOujlGFMAAADwjGg0qrKyMv3ud7+LdxWAh/kBAADAGyorK5Wfn6+bb75Zl19+ebzrAJyZAgAAgDcUFBRo9+7d8a4BxHBmCgAAAAAsMKYAAAAAwAJjCgAAAAAsMKYAAAAAwAJjCgAAAAAsMKYAAAAAwAJjCgAAAAAsMKYAAAAAwAJjCgAAAAAsMKYAAAAAwIIv3gWAnsIvyXEh1+dSrlezvdjZzWw6ez+bzt7P9mJnN7P9LmQC3RVjCugkIUnGhVzHpVyvZnuxs5vZdPZ+Np29n+3Fzm5muzX+gO6Ih/kBAAAAgAXGFAAAAABYYEwBAAAAgAXGFAAAAABYYEwBAAAAgAXGFAAAAABYYEwBAAAAgAXGFAAAAABYYEwBAAAAgAXGFAAAAABYYEwBAAAAgAXGFAAAAABYYEwBAAAAgAXGFAAAAABY8MW7AOA1xhgFg0GFw2HV1dWpqalJzc3N8a4FAEC3MW7cuDaXs7KyVFpaGqc2gHs4MwW0IxwO68SJEwqFQtq1a5e2bt2qpqYmrV+/Xh988IHC4bAikYgGDBiglJSUeNcFAKDbqKioiP1ZsGCBqqurlZ+fr4ULF7b7Mb///e/lOI4qKiq6sCnw2XBmCr1eU1OTmpqaFAwG9eGHH6qpqUmNjY3atGmTUlNTFY1GlZ6erkGDBqmxsVGTJ0+WJK1bt07Dhw+XJCUkJMgvyXGhn8+lXK9me7Gzm9l09n42nb2f7cXObmb7z3g7Eolo7ty5euutt5STk6Pi4mKVlJSooKCgzcecPHlSP/vZzzRhwgQXGgHuYUyhVzl8+LACgYA2bdqk1tZWNTY2qqqqSqmpqXIcR9nZ2UpNTVVFRUXsC/q6des0aNAgSZLjtP/PTkiScaGz41KuV7O92NnNbDp7P5vO3s/2Ymc3s8/8l7K8vFz5+fnKy8uTJM2aNUvLly8/a0w9+eSTevTRR/XjH//YhUaAe3iYH3oVn88nn8+ngoICTZo0SX379tU111yjUaNGye/3q1+/fkpKSop3TQAAeoT6+nrl5ubGLufk5Ki+vr7NMZs3b1Ztba2++MUvdnU94DPjzBR6lYyMDPl8PvXp0yfeVQAA6PWi0agefvhhvfzyy/GuAljhzBQAAABckZ2drdra2tjluro6ZWdnxy6fPHlS27dv14033qgRI0aorKxMJSUlvAgFPIMxBQAAAFcUFxerqqpK1dXVCgaDWrZsmUpKSmLXZ2RkqKGhQTU1NaqpqdHEiRO1YsWKs15aHeiuGFMAAABwhc/n06JFizR16lRdeeWVmjFjhgoLCzVv3jytWLEi3vWAz4znTAEAAMA106ZN07Rp09q8b/78+ec8du3atV3QCOg8nJkCAAAAAAuMKQAAAACwwJgCAAAAAAuMKQAAAACwwJgCAAAAAAuMKQAAAACwwJgCAAAAAAuMKQAAAACwwJgCAAAAAAuMKQAAAACw4It3AaCn8EtyXMj1uZTr1WwvdnYzm87ez6az97O92NnNbL8LmUB3xZgCOklIknEh13Ep16vZXuzsZjadvZ9NZ+9ne7Gzm9lujT+gO+JhfgAAAABggTEFAAAAABYYUwAAAABggTEFAAAAABYYUwAAAABggTEFAAAAABYYUwAAAABggTEFAAAAABYYUwAAAABggTEFAAAAABYYUwAAAABggTEFAAAAABYYUwAAAABggTEFAAAAABYYUwAAAABgwRfvAkBP4ZfkuJDrcynXq9le7OxmNp29n01n72d7sbOb2X4XMoHuijEFdJKQJONCruNSrlezvdjZzWw6ez+bzt7P9mJnN7PdGn9Ad8TD/AAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACz44l0A6Cn8khwXcn0u5Xo124ud3cyms/ez6ez9bC92djPb70Im0F0xpoBOEpJkXMh1XMr1arYXO7uZTWfvZ9PZ+9le7OxmtlvjD+iOeJgfAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABV+8CwAAAKBnGTduXJvLWVlZKi0tjVMbwD2cmQI+g2AwqF27dqmxsTHeVQAA6DYqKipifxYsWKDq6mrl5+dr4cKFZx373HPPqaCgQEVFRbr55pu1Z8+eODQG7DCmAAtNTU1qaWlRRUWFkpOTlZaWFu9KAAB0O5FIRHPnztWqVatUWVmppUuXqrKyss0x1157rSoqKrRt2zZNnz5d3/nOd+LUFrh4jCmgg4wxCofD2rx5syorK+X3+zVp0iTl5ubKcZx41wMAoNspLy9Xfn6+8vLylJSUpFmzZmn58uVtjpkyZYpSU1MlSRMnTlRdXV08qgJWGFNAB+zfv18bNmxQKBTSZZddpuLiYvl8PkYUAADnUV9fr9zc3NjlnJwc1dfXt3v84sWL9YUvfKErqgGdghegANoRDodVW1urxsZGnThxQtdcc402b96sjIyMeFcDAKDHWbJkiSoqKvTuu+/GuwrQYYwp4FNaWloUCAS0YcMGZWdnKy0tTaNHj77gx/kluXGeyudSrlezvdjZzWw6ez+bzt7P9mJnN7P9Z7ydnZ2t2tra2OW6ujplZ2ef9TGrV6/WM888o3fffVfJyckutALcwZgC/k8kEtH777+vQCCgxMRETZo0SQkJCdq3b1+HPj4kybjQy3Ep16vZXuzsZjadvZ9NZ+9ne7Gzm9lnDrTi4mJVVVWpurpa2dnZWrZsmV599dU2x2/ZskUPPvigSktLdemll7rQCHAPz5lCr/fJJ5+ovLxcra2tGj58uCZMmCC/36+EBP73AADgs/D5fFq0aJGmTp2qK6+8UjNmzFBhYaHmzZunFStWSJIeeeQRNTY26o477tA111yjkpKSOLcGOo4zU+iVIpGI6uvr1djYqIaGBhUWFmrr1q3KzMyMdzUAAHqUadOmadq0aW3eN3/+/Njbq1ev7upKQKfhR+/oVVpbW9Xa2qr169crFAopLS1NBQUF/J4oAAAAXDTOTKFXOXjwoBzH0eTJk5WQkKCDBw/GuxIAAAA8ijNT6FWGDRumpKQkng8FAACAz4zvKAEAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACwwpgAAAADAAmMKAAAAACz44l0A6Cn8khwXcn0u5Xo124ud3cyms/ez6ez9bC92djPb70Im0F0xpoBOEpJkXMh1XMr1arYXO7uZTWfvZ9PZ+9le7OxmtlvjD+iOeJgfAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABcYUAAAAAFhgTAEAAACABV+8CwA9hV+S40Kuz6Vcr2Z7sbOb2XT2fjadvZ/txc5uZvtdyAS6K8YU0ElCkowLuY5LuV7N9mJnN7Pp7P1sOns/24ud3cx2a/wB3REP8wMAAAAAC4wpAAAAALDAmAIAAAAAC4wpAAAAALDAmAIAAAAAC4wpAAAAALDAmAIAAAAAC4wpAAAAALDAmAIAAAAAC4wpAAAAALDAmAIAAAAAC4wpAAAAALDAmAIAAAAAC4wpAAAAALDAmAIAAAAAC754FwB6Cr8kx4Vcn0u5Xs32Ymc3s+ns/Ww6ez/bi53dzPa7kAl0V4wpoJOEJBkXch2Xcr2a7cXObmbT2fvZdPZ+thc7u5nt1vgDuiMe5gcAAAAAFhhTAAAAAGCBMQUAAAAAFhhTAAAAAGCBMQUAAAAAFhhTAAAAAGCBMQUAAAAAFvg9UwAAAOhU48aNa3M5KytLpaWlcWoDuIczU0AniEQi8a4AAEC3UVFREfuzYMECVVdXKz8/XwsXLjzr2NbWVs2cOVP5+fmaMGGCampqur4wYIkxBXwGxhjt3r1bgUAg3lUAAOh2IpGI5s6dq1WrVqmyslJLly5VZWVlm2MWL16szMxM7dq1S//8z/+sRx99NE5tgYvHmAIsGWO0efNmBYNBpaWlxbsOAADdTnl5ufLz85WXl6ekpCTNmjVLy5cvb3PM8uXLde+990qSpk+frjVr1sgYE4+6wEVjTAEWjh49qqamJuXm5uqKK66Idx0AALql+vp65ebmxi7n5OSovr6+3WN8Pp8yMjJ0+PDhLu0J2OIFKICLtHv3bh06dEipqam69NJL410HAAAAccKZKaCDgsGgmpubFQqFVFxcrIQE/vcBAOB8srOzVVtbG7tcV1en7Ozsdo8Jh8M6fvy4BgwY0KU9AVt8Nwh0wJEjR7Rx40YlJSVp9OjRDCkAADqguLhYVVVVqq6uVjAY1LJly1RSUtLmmJKSEv3mN7+RJL3++uu66aab5DhOPOoCF42H+QHnYYxRa2urqqqqNHbsWG3ZsiXelQAA6PbO/D1TiYmJuvLKK5WQkKDHH39chYWFmjdvnsaNG6eSkhLdd999uvvuu5Wfn6/+/ftr2bJlcWwOXBzGFNCOYDCobdu2yRhz3of1GWNkjFFm375yGhs7vYdfkls/n/Nithc7u5lNZ+9n09n72V7s7GZ2ckKC7rvvPn3ta19r9wzT/PnzY2+npKTod7/7nQtNAPcxpoBzCIfD2rhxo0aNGqWqqqrzDqlwOKzExES9/qmXej2ttbVVkUhEffr04WELncwYo5aWFiUnJysxMTHedXqcSCSiYDCoPn36xLtKj9TS0qKkpCT+7rogEokoEAgoNTWVr7ud7PTX3cTERCUnJ7e57rvf/a6OHz8uY4zmz5+vJ598UsOGDTvr39CsrCyVlpZ2ZW3ANYwp4Aynfwlva2urbrjhBqWkpKiqqqrd40OhkCRp7NixZ10XDoe1Y8cOZWZm6rLLLuMfdBfs2rVLSUlJGjZsWLyr9Ei1tbVKSEg468ni6Bz79u1TOBzm769Lamtr1dLSolGjRsW7So9z+t/KpqYmFRQUyOc79e3kmjVrYsdEo1EtXrxYv/3tb/Xqq6/qsssui1ddwFWMKeD/GGO0adMmXXLJJUpLS1NKSkq7x0WjUTmOo40bN57zmGg0GvupcyAQUEVFhZvVe6VwOKxgMKjU1FQdPHgw3nV6pNNn/fbt2xfvKj1SNBpVa2srf39d1NLSosOHD8e+2UfnCoVCeu+999SnT582Z59On6GSTv09nzBhgjIzM5WRkXFWBmep4HV8dQEkHT58WE1NTRo1apQGDhyohoaGdo+NRCKKRqO67rrrznn9kSNHtGvXLo0dO1aXXHKJW5V7tdbWVm3dulWTJk1SUlJSvOv0SMYYlZeXa/z48ZxVdcnp+3jcuHHcxy4JhULasmWLrrrqqnZ/QIbP5uTJk9qxY4fy8vJiL2d+5hkqSTp06JDuu+8+3XDDDfr+97/PQ1vRozCm0Ovt2rVLR44cUWpqqgYOHNjucdFoVAkJCdqwYUO7xwSDQYXDYaWkpOijjz5yoy4kNTc3KykpSVu3bo13lR7r9FkTzqq6q7W1VRs2bOCbSxdFo1Ft2LCB5626bPv27fL5fGf9gOvMs1RLlizRf/zHf2jIkCHnPFvIWSp4EWMKvVZra6uamppkjNG4ceNUVlbW7rHRaFThcPicz406ff3p8cTvoXLX7t27lZWVpZEjR8a7So9WV1cnScrJyYlzk56tvr5e0WhUubm58a7So9XU1CgSifC8HRdFo1FVVVUpEolo9OjRsR8QnHmWyhijP/7xj1q4cKGef/55jR8/Pl51gU7DmEKvdPjwYf3tb39TcnKyLr/88naPM8YoISFB69evb/eYaDSqQCAQ+4ncpk2b3KgMtX2e1PkeionP7vRz/vbv3x/vKj3a6TOABw4ciHeVHq+5uVmffPIJz59yWTAY1HvvvaeUlJSzfrB4+ixVNBrVHXfcoYSEhNhDAz+Ns1TwCr6ioFc5/Ut4d+/ereuuu06bN28+77HnOxslScePH9dHH32koqIiZWZmulEZ/ycYDOr999/XxIkTz3o5XnQuni/VdXjeVNc5/VzLq6++mudauuzYsWPauXOnRo0a1eZFJ848S9XY2KiHHnpIl1xyiX7xi1/wnDZ4Fo9FQq+yc+dOSad+M/uFvnCHQiEZY9q9fv/+/aqqqtJVV13FkHKZMUY7duzQZZddxpDqAi0tLfx+ni7iOI7S0tLU3Nwc7yo9XnJysvLz81VZWXner+347Pr166eioiJVVVW1+2qgffv21a9//WuNHj1aU6ZMUXV1dRe3BDqHc4EvKHy1QY9hjFFzc7PKy8s1efJkSdK6devavD1p0iRFo1GVl5crGo22m9Pa2ipjjFJSUviGswsEg0EZYxhSXSQYDEoSP73vItzfXau1tVWS+HrSSU6cOKF/+Zd/0cGDBzVo0CDNmzdP6enpkk79exkIBOQ4jt59913913/9lyTpq1/9qt555x0dP35cgUBABw4cUDgc1v/f3r0HRXXf/x9/7S4sd0FQ8YpKkESJCVqJWscYqak3wniJJiaaTsWOVaOx3orpxNZL1WRaY2yc0bTGW0YtXhJJtUwbLTo6GiQjiQIKUkC5qKCAwALLHvb3R8ad5tev3+R7UiEpz8eMfwjM8cxxRvc5n895f4KCghQVFSVJqqqqUllZmRobGzVs2LCvbLffsGGDduzYIZvNpi1btmjs2LGSpLS0NL322msyDENz5sxRcnJyKz8N/Bd74Ic9tvmhXfm6wRBfN/a8ublZ2dnZ6tKli3r37k1ItYKamhpdu3ZNgwYNYrBHK8nOzlZERITnAxEerrq6OhUVFenxxx9v61tpF1paWpSVlaXIyEiFhIS09e18773xxhtKTEzUkiVLtGnTJqWnp2vNmjWe77vdbl26dEl79+7V6dOnZbfbNWrUKJ06dUodO3bUM888o/fff18REREaOnSoHnvsMe3evVt5eXmyWq2aO3eufve733mul5OTowMHDig7O1tlZWUaM2aMZ9fJggUL9Pe//109e/ZUXFycEhMTNWDAgFZ/JmhfiClA32zsuWEYamxslI+Pj5qbm1VRUdGKd9g+3V9N9PPzY7BHK6qvr2fbWSurr69/4CHg+M+7H1RsZ/32Dh8+rE2bNunChQuKiIjQhg0bdPDgwa+sUp08eVKDBg1Sbm6ufH19FRwcrNjYWPn4+MjpdGrlypWqqamR1WrV/v37deDAAXXs2FG9e/fW1atX9fLLL6u8vFxOp1M2m01Lly6Vj4+PDh06pIqKCvXv318ul0uFhYUKCQmR3W7X9evXFR8fr65du8rLy4tjHvDQEFNo975u7Ln05YGDRUVFiouLU0BAQCveXfvldrt1+fJl9enTR126dGnr22k3GhoalJ+fryeeeKKtb6VduXTpkiIjI/n3pRVVVFSovLxcAwcOJKi+hdraWo0bN06SdPToUUlSbm7uV1apzp07p8GDB+upp57SuXPnVFhYqFdffVXDhw/XtGnTdOjQIXXs2FFDhgxRVFSU5s2bp4ULF+qVV17RkSNHFBERoQEDBig5OVnDhw/3DI9avny5rly5ovHjxyszM1N79uxRaGioJMnLy0sTJ07Ujh072ubBoN0gptBuud1uz8S+/+1nCgsLVVtbq9jYWHl7e7fiHbZvpaWlstvthFQrq6qqYutTGwgJCVF1dTUx1Yo6d+6sqqoqlZSUcM7X10hMTNStW7f+7eurVq36yu+PHz/ueRdtwoQJGjVqlFJTU2WxWDRp0iTPsRbdu3dXQ0ODAgMD5ePjo7i4OPn5+enevXvq27evpkyZovPnz2vJkiWyWq367LPPtHHjRklS3759dfjwYfXr1099+vRReHi4JOnYsWO6ffu2YmNjJekrq+u8S4WHiZhCu3Q/omw22wO31tx/cdZqtcrHx0dZWVmtfJft1/0tlf7+/mx9amX3z5f6nz444eExDENOp/OBk8/wcLjdbpWVlam0tNRzyCz+3RtvvPHA7wUFBSktLU1hYWEqKytTaGioLly4oO3bt8vlcumPf/yj1q1bpw8//FATJkxQZmamWlpa5O3trdzcXN27d0+vvPKKBg4cqOTkZGVmZmrIkCEqKiqSt7e3DMNQXV2dnn76aYWEhOjq1atyOp0KDg5Wbm6uzpw5o6SkJBUUFCgkJMTzf3VoaKjS0tI0ePBg3bhxQ59++invUuGhIKbQLjU3N0vSA7f2ORwOZWdnKzo6Wl27dm3NW2v3XC6XLl68yJbKNnL/zCOGfbQuzptqO/f/vR80aBAH+n4D//8qlcPh0IIFC7RlyxYZhqGpU6cqLi5Oc+fOlZ+fn+Li4vTmm2/qhz/8oaKiohQWFqZbt25p/vz5+vDDD2W322Wz2fSDH/xANptNM2fO1IIFCxQfH6+YmBjV1dXp/Pnzqqur04kTJ/TMM8/I4XDo7NmzysrK0siRI3Xz5k09+eST+vzzz1VYWKgePXqoa9euOnjwoIqKivTiiy+qpKREkZGRevHFF3X06FFiCv8xjEZHu+F2u1VfX6/MzEwZhvG//sWIv7QAABA9SURBVGxTU5O8vb35QNkGDMOQ2+3mQ00baW5uZjtrG+HZtx2XyyWLxcLqlAk1NTVat26dbt++rcrKSm3dulV9+vRRQkKCbDabjh49qpqaGj3//POyWq2yWCyKjo7Wli1blJKSorS0NDkcDlmtVt25c0cBAQHy8fFRY2Oj6uvrPVvypS8n8rrdblmtVs/fldPplNVqlZeXlwzDkM1mk9vtVocOHdStWzc5nU5ZLBbNmTNHy5YtU+fOndXS0qJevXoxmAL/F4xGB+4fjjlq1Ki2vhUAAL73nn32Wd28eVPSl+/9NTY26vXXX9f27dslSfPmzVN8fLxWrFghHx8fORwO/frXv9Y777yjQYMG6ezZs7p3756uXbsmSQoPD9f06dP1wgsvaOzYsdq3b5+mT5+uTp06KTAwUHl5efL399ekSZN06NAhrVixQlu2bJG/v79+/vOfq6GhQW+//bbWr1+v9957T5cuXdLy5cu1efPmrxw9MGnSJAZT4D+GlSkAAAB8a3fu3NH06dN1/fp1lZWV6cKFCxowYIB69uypu3fvqlevXnI6naqsrFR4eLhqa2vVv39/paena8+ePZo3b56ampo8q0u7du3S6NGjFR0drYaGBs/K1OTJk5WSkqJu3bqpoqJCfn5+WrhwoQ4fPqyrV6/q1KlTGj16tLy8vOTl5aWmpiYZhqGmpiYFBQUpMDBQ/fr105///Gf16dOnrR8bvh8euDLFHiYAAAB8a2FhYTpx4oTy8/M1f/58paamSpIqKytlt9v117/+VVeuXJHD4VBqaqpyc3NVXFysqqoqpaSkyOVy6fbt29q9e7e8vLx07tw5nT59Wg0NDZ5Q8vHx0eeffy7DMHTr1i3Nnj1bd+7c0dGjRz0DXNauXavQ0FA1NTXpiSeekLe3twoLC7V9+3YZhqGuXbvq9u3bev7559vyceG/BCtTAAAA+FbGjBnj2fInffn+640bNxQUFKSKigo9/fTTOnnypDIzMzVs2DCtXbtWK1eu1Pvvv6/169fr+vXrGj58uE6dOiWXy6WgoCB16NBBjY2NkqTq6mpZLBYNHjxYBQUFCgoKUllZmSorKxUaGqrExET95S9/0eOPP67c3FydOHFChmHopz/9qYKDg9XQ0KCSkhJNmTJFH3zwgcrKyhQREaETJ06w/R/fBCtTAAAAeDg++eQTXb582fMrNzdXdXV1Ki8vV5cuXTyTcXv06KEOHTrorbfeUlRUlG7fvq1r164pPDxcPXv2lPRliLlcLrndbtntdtntds+Uy+rqajU3N6ulpUV+fn5qamryXNdisai2tlYdOnRQSEiI5syZo379+unKlSuy2+3y8fHR2rVrJUl5eXkyDEMvvPCCYmNjtWbNmjZ4avhvQEwBAADgoRkyZIjy8/MlSTt37lRTU5MSEhKUk5Oj/fv3KycnR4GBgTp9+rSqqqr0hz/8QYZh6OzZsxo8eLAaGhp0/vx5ZWdn68aNG3r11Vf1q1/9Si6XSzt37pRhGNq7d68ee+wxnT17VrW1tUpISNDevXs1c+ZMRUVF6dKlS2ppadGuXbskyfMO1jvvvKOsrKx/O4AY+KbY5gcAAICHJi0tTS+//LJCQ0MVEhKiu3fvas6cOXr22Wf10ksvqaqqSvX19Ro6dKhu3Ljhefepvr5eq1ev1tq1a2Wz2dTS0iJ/f3+lpaXJYrEoPj5ebrdbhmHIarVqxYoVWrNmjby8vOR2u+Xt7a2goCA1NzerpqZGQUFBcjgcstvtntWv+0elvPbaazp+/Lj8/f21a9euB55DiXaLbX4AAABofWPGjFFwcLD+9re/aenSpaqqqlJiYqIGDRqkmpoajRs3Ths2bNAXX3yh1NRUhYWFKT4+XhaLxRM7hYWFGj58uJxOp4YOHaqAgAA5nU5t3LhRv//97+VyuTRt2jS1tLTI29tbI0aMUG1trdxutxYtWuTZGnh/pSs5OVne3t6KjY1VXFycLl68qPz8fL333nuaN29eGz8xfJ9wzhQAAAAeGi8vL7377rsaO3asampqFBkZqZiYGM2ePVu+vr4KDg7W3Llz9ac//UkjR45UQ0ODVq5cKUnKyMhQQECARowYoaqqKlmtVlmtVh07dkyRkZF688031dLSosDAQNXX1ysjI0O9e/dWXl6eBgwYoO7du8tut2v+/PmKiIiQl5eXoqKiFBwcrMzMTA0cOFDjx4/XxYsXZbFYNGzYMFVXV6u8vFzdunVr4yeH7wNWpgAAAPBQTZgwQXl5efroo48UFhbm+VqXLl3Uo0cP+fr6aty4cWppaZEkffDBB5Kk0tJS2Ww2FRYWatmyZTIMQ5GRkdq8ebOsVquOHDmijz76SJI0efJkTZ06VW63W4sXL9a1a9c0btw4rV+/Xvv375efn5/u3r0rf39/3bx5UzNnzlRsbKz+8Y9/qLq6WpWVlUpPT1dBQYFGjRrFYAp8I8QUAAAAWkVcXJzy8/NVWFgol8ulgoICJSYmyjAM7dmzRwkJCdqwYYNSUlKUk5OjmpoajRgxQhaLRU6nUy6XS2fOnFF0dLSKi4v11FNPKSAgQHV1dUpOTlZycrIKCgo0ceJEGYah7du3KyAgQKWlpWpqatKRI0d04cIFjRgxQlOmTFFWVpYCAwNlt9s9kRcSEqJ9+/YxmALfCAMoAAAA0GqOHz+uxYsXq76+Xv7+/srPz9fs2bOVlpamhQsX6he/+IXi4uJUVFQkh8Ohzp07a/HixTp58qROnz6t7t276969e6qtrdWkSZP0ySefqLa2VmFhYbJaraqqqtJPfvITffzxxyotLVVgYKAeeeQRFRYWyuFwSJJnBax///7KycnRokWLlJ2drdzcXJWXl+vy5cvq37+/3G43wykgMYACAAAA3wX3t/wVFxfLMAwVFhbqxz/+sRobG5WYmChfX18tW7ZMVqtVq1at0nPPPaf9+/friy++0JgxY/TPf/5Tp06dkmEYstvtmjhxonx9fTVy5EgVFxfLYrHo448/1oYNGzxnTn322Wdat26dnnvuOZ05c0ZWq1WTJ0/WihUrNHz4cKWmpio+Pl6vv/663G63Ro0apfHjx2vbtm3Kz89nOAUeiJUpAAAAtIn7q1Q1NTXq1auXMjMztWrVKl29elXnzp1TXl6eZs2apZMnT+revXtKT0/XiBEjJEne3t7q1KmTGhoalJSUpN27d6uiokLR0dG6efOm/P391atXL4WFhek3v/mNkpKSdOvWLblcLjU1NamyslLTpk1TUlKSfvazn6lDhw4KCAjQb3/7WyUnJ+vtt9/WjBkztG3bNs2YMUOS9Oijjyo9PZ3hFO0PK1MAAAD4bvmfBlOsWbNGNptN4eHh8vX11cGDB7V582Z169ZNVuuXH11dLpfcbrcyMjL0y1/+Up06dVJwcLDu3LmjpqYmLVmyxHPNnj17qrS0VLNmzdLo0aM1ePBgRUVFqbGxURkZGUpISJDb7VZxcbFycnI0adIk3bp1SxMmTFBzc7OCgoI893v/WsB9xBQAAADa1L8OpnA6nTp79qwiIiK+8jN9+/bV7t27JUmHDh1SQECALBaLEhMTdeDAAU8QVVdX65FHHvFcs7a2Vi6XSzt37lRWVpY2b97suUZCQoJ8fX09f8bNmzclSRaLRRkZGXK73QoODm6lp4DvI2IKAAAAbepfz6Lq37+/xo0bp7q6Oq1atUqpqakqKSnRmDFjdOfOHUVFRWnTpk168skndePGDcXExGjq1KkqLi7WjBkzNGXKFJWWlnqueezYMS1dulSBgYFyOByaMWOGCgoKtHr1as/2vfDwcJWXl+vQoUN69NFH5XA4tGjRIo0dO1YlJSWe+ywpKVGPHj3a6jHhO4h3pgAAAPCd4nK5FB0drRMnTqhHjx6Ki4vTvn37FBMT4/mZrVu36tKlS9q2bZsOHDigI0eOKCUlRdnZ2XrppZeUkZGhsrIy/ehHP1J+fr5sNpskqaioSAkJCbp8+bLnWsuXL1dYWJiSk5O1ceNG3b17V2+99ZaOHTumd999V8ePH9enn36qRYsWKSMjo9WfB9rcA9+Z8mrNuwAAAAC+zr+uVBmGodmzZysmJkarVq3SkCFDlJiYqKSkJM2aNUtRUVEKDQ3VgQMHJEkxMTGaPn26BgwYIC8vL23dutUTUjNmzFB6eroqKyvVs2dPrV69WklJSUpOTtb06dO1Y8cO9e7dWykpKZK+fKfr+PHjioqKkr+/v3bu3NlmzwTfTaxMAQAAAMCDMc0PAAAAAP6TiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATiCkAAAAAMIGYAgAAAAATvL7m+5ZWuQsAAAAA+J5hZQoAAAAATCCmAAAAAMAEYgoAAAAATCCmAAAAAMAEYgoAAAAATCCmAAAAAMCE/wdMMsmVkA8/zwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.plot_grid(g, figsize=(15, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define the problem data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We declare the permeability matrix $K$, the scalar source term $f$, and the boundary conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Permeability\n",
    "perm = pp.SecondOrderTensor(np.ones(g.num_cells))                     \n",
    "\n",
    "# Unitary scalar source already integrated in each cell\n",
    "f = g.cell_volumes\n",
    "\n",
    "# Boundary conditions\n",
    "b_faces = g.tags['domain_boundary_faces'].nonzero()[0]\n",
    "bc = pp.BoundaryCondition(g, b_faces, ['dir']*b_faces.size)\n",
    "bc_val = np.zeros(g.num_faces)\n",
    "\n",
    "# Collect all parameters in a dictionary\n",
    "parameters = {\"second_order_tensor\": perm, \"source\": f, \"bc\": bc, \"bc_values\": bc_val}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once all the data are created we group them in a dictionary, where the keyword `\"flow\"` represent the physical process considered and ensures that the discretization objects use the correct parameters. Note that the call to initialize_default_data assignes default values to the flow parameters which are not specified. This means that we could have omitted the specification of `\"bc_values\"` and `\"second_order_tensor\"`, since we have assigned the default values for these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_key = \"flow\"\n",
    "data = pp.initialize_default_data(g, {}, data_key, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we present all the approaches to solve the problem.<br>\n",
    "**Note**: The problem data are not stored in the solver class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two-point flux approximation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two-point flux approximation solves the single-phase flow problem in the primal formulation. We use this to discretize the flux term, before assembling it. The source term is discretized using the FV Integral class. The pressure is directly computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "flow_discretization = pp.Tpfa(data_key)\n",
    "flow_discretization.discretize(g, data)\n",
    "A, b_flow = flow_discretization.assemble_matrix_rhs(g, data)\n",
    "\n",
    "rhs_discretization = pp.ScalarSource(data_key)\n",
    "rhs_discretization.discretize(g, data)\n",
    "_, b_rhs = rhs_discretization.assemble_matrix_rhs(g, data)\n",
    "\n",
    "p_tpfa = sps.linalg.spsolve(A, b_flow+b_rhs) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To represent the solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.plot_grid(g, p_tpfa, figsize=(15, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consistency check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert np.isclose(np.sum(p_tpfa), 14.192684340967551)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-point flux approximation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The multi-point flux approximation solves the single-phase flow problem in the primal formulation. We discretize the flux term anew with the MPFA, and assemble the matrix. Note that the call to Mpfa.discretize() will override the already stored discretization matrix for Tpfa. We reuse the source term discretization, as this is the same for TPFA and MPFA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "flow_discretization = pp.Mpfa(data_key)\n",
    "flow_discretization.discretize(g, data)\n",
    "A, b_flow = flow_discretization.assemble_matrix_rhs(g, data)\n",
    "\n",
    "p_mpfa = sps.linalg.spsolve(A, b_flow+b_rhs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To represent the solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ivar/.local/lib/python3.7/site-packages/mpl_toolkits/mplot3d/axes3d.py:753: UserWarning: Attempting to set identical bottom==top results\n",
      "in singular transformations; automatically expanding.\n",
      "bottom=0.0, top=0.0\n",
      "  'bottom=%s, top=%s') % (bottom, top))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.plot_grid(g, p_mpfa, figsize=(15, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consistency check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert np.isclose(np.sum(p_mpfa), 14.192684340967542)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dual virtual element method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dual virtual element method solves the single-phase flow problem in the dual formulation. The pressure and the normal flux to each face are directly computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "flow_discretization = pp.MVEM(\"flow\")\n",
    "flow_discretization.discretize(g, data)\n",
    "A, b_flow = flow_discretization.assemble_matrix_rhs(g, data)\n",
    "\n",
    "rhs_discretization = pp.DualScalarSource(\"flow\")\n",
    "\n",
    "data[pp.PARAMETERS][\"flow\"][\"source\"] = data[pp.PARAMETERS][\"flow\"][\"source\"]\n",
    "rhs_discretization.discretize(g, data)\n",
    "_, b_rhs = rhs_discretization.assemble_matrix_rhs(g, data)\n",
    "\n",
    "up = sps.linalg.spsolve(A, b_flow+b_rhs)                                              \n",
    "\n",
    "# Extract the normal flux and pressure from the solution\n",
    "u_mvem = flow_discretization.extract_flux(g, up, data)\n",
    "p_mvem = flow_discretization.extract_pressure(g, up, data) \n",
    "\n",
    "# For visualisation purpose project the flux on a vector piecewise constant field\n",
    "P0u = flow_discretization.project_flux(g, u_mvem, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To represent the pressure and velocity solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.plot_grid(g, p_mvem, P0u * 0.2, figsize=(15, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consistency check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert np.isclose(np.sum(p_mvem), 14.348068220560325)\n",
    "assert np.isclose(np.sum(u_mvem), 0)\n",
    "assert np.isclose(np.sum(P0u), 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fractures\n",
    "We now demonstrate how single-phase flow may be simulated for a domain containing a fracture. The process is very similar to the single-dimensional case. Since the fracture is treated as a lower-dimensional subdomain, we make a separate grid representing it, in addition to the matrix grid. The grids are collected in a \"grid bucket\", and assignment of parameters is handled by looping over the grids in the bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb, _ = pp.grid_buckets_2d.single_horizontal([Nx, Ny], x_endpoints=[0.2, 0.8], simplex=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We choose slightly different parameters from the case above in order to see the impact of the fracture, to which we assign very low permeability. We also have to assign the normal diffusivity parameter on the grid bucket edge between the matrix and the fracture."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Thickness of fracture\n",
    "aperture = 1e-3\n",
    "fracture_perm = 1e-5\n",
    "\n",
    "for g, d in gb:\n",
    "    # The concept of specific volumes accounts for the thickness\n",
    "    # of the fracture, which is collapsed in the mixed-dimensional\n",
    "    # model.\n",
    "    specific_volumes = np.power(aperture, gb.dim_max()-g.dim)\n",
    "    # Permeability\n",
    "    k = np.ones(g.num_cells) * specific_volumes\n",
    "    if g.dim < gb.dim_max():\n",
    "        k *= fracture_perm\n",
    "    perm = pp.SecondOrderTensor(k)                     \n",
    "\n",
    "    # Unitary scalar source already integrated in each cell\n",
    "    f = 1e-2* g.cell_volumes * specific_volumes \n",
    "\n",
    "    # Boundary conditions\n",
    "    b_faces = g.tags['domain_boundary_faces'].nonzero()[0]\n",
    "    bc = pp.BoundaryCondition(g, b_faces, ['dir']*b_faces.size)\n",
    "    bc_val = np.zeros(g.num_faces)\n",
    "    bc_val[b_faces] = g.face_centers[1, b_faces]\n",
    "\n",
    "    parameters = {\"second_order_tensor\": perm, \"source\": f, \"bc\": bc, \"bc_values\": bc_val}\n",
    "    pp.initialize_data(g, d, data_key, parameters)\n",
    "\n",
    "for e, d in gb.edges():\n",
    "        gl, _ = gb.nodes_of_edge(e)\n",
    "        mg = d[\"mortar_grid\"]\n",
    "        # Division through aperture/2 may be thought of as taking the gradient, i.e.\n",
    "        # dividing by the distance from the matrix to the center of the fracture.\n",
    "        kn = fracture_perm / (aperture/2)\n",
    "        pp.initialize_data(mg, d, data_key, {\"normal_diffusivity\": kn})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we declare the primary variables and assign the discretization for the diffusive and source term for all grids, and for the coupling law on the edge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "flow_discretization = pp.Tpfa(data_key)\n",
    "source_discretization = pp.ScalarSource(data_key)\n",
    "for g, d in gb:\n",
    "        d[pp.PRIMARY_VARIABLES] = {\"pressure\": {\"cells\": 1}}\n",
    "        d[pp.DISCRETIZATION] = {\"pressure\": {\"diffusive\": flow_discretization,\n",
    "                                             \"source\": source_discretization}}\n",
    "        \n",
    "flow_coupling_discretization = pp.RobinCoupling(data_key, flow_discretization)        \n",
    "for e, d in gb.edges():\n",
    "    g1, g2 = gb.nodes_of_edge(e)\n",
    "    d[pp.PRIMARY_VARIABLES] = {\"mortar_flux\": {\"cells\": 1}}\n",
    "    d[pp.COUPLING_DISCRETIZATION] = {\n",
    "        \"lambda\": {\n",
    "            g1: (\"pressure\", \"diffusive\"),\n",
    "            g2: (\"pressure\", \"diffusive\"),\n",
    "            e: (\"mortar_flux\", flow_coupling_discretization),\n",
    "        }\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The assembler takes care of discretization and assembly of the matrices. Note that `solution` vector contains all primary variables: the pressure in both matrix and fracture, as well as the fluxes between the two grids. The ordering is also handled by the assembler."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "assembler = pp.Assembler(gb)\n",
    "assembler.discretize()\n",
    "A, b = assembler.assemble_matrix_rhs()\n",
    "solution = sps.linalg.spsolve(A, b)\n",
    "assembler.distribute_variable(solution)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the solution and perform a consistency check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pp.plot_grid(gb, \"pressure\",figsize=(15, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_2d = gb.grids_of_dimension(2)[0]\n",
    "d_2d = gb.node_props(g_2d)\n",
    "p_2d = d_2d[pp.STATE][\"pressure\"]\n",
    "assert np.isclose(np.sum(p_2d), 200.14208519866756)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
